PREDICTING CONSUMER BEHAVIOR THROUGH AI-DRIVEN PSYCHOLOGICAL PROFILING: A COMPREHENSIVE ANALYTICAL PERSPECTIVE

Abstract

The study will look at how AI-based psychological profiling can be used to predict consumer behavior including how demographic factors can impact perceptions and acceptance of major machine learning strategies. The study compares three major AI methods, including supervised learning, unsupervised learning, and deep learning, to learn how gender and marital status determine consumer attitude towards these technologies. Findings represent that the perception of supervised and unsupervised learning tends to be more positive than deep learning, which implies the greater familiarity and confidence in less complex, interpretable AI-based models. Unsupervised and deep learning had a great gender difference, with differences in levels of technological awareness and engagement. Marital status was found to be a better predictor and married people were most tolerant of supervised and unsupervised learning and unmarried people of deep learning approaches. These results underscore the importance of incorporating demographic nuances into AI-driven marketing strategies. By understanding how consumer segments respond to different AI models, organizations can enhance predictive accuracy, personalize marketing efforts, and design more ethical and effective digital engagement frameworks. Ultimately, the study contributes to the growing body of knowledge on consumer analytics by linking machine learning methodologies with psychological profiling and demographic insights.